docs: make Chinese README the default

README.md

@@ -1,9 +1,15 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/lightgbm-org/LightGBM) · [上游 README](https://github.com/lightgbm-org/LightGBM/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <img src=https://github.com/lightgbm-org/LightGBM/blob/main/docs/logo/LightGBM_logo_black_text.svg width=300 />
 
 > [!NOTE]
-> This project moved from `Microsoft/LightGBM` to `lightgbm-org/LightGBM` in March 2026.
-> This repository is still the official LightGBM source code, managed by the same maintainers (including the creator of LightGBM).
-> For details, see https://github.com/lightgbm-org/LightGBM/issues/7187
+> 本项目已于 2026 年 3 月从 `Microsoft/LightGBM` 迁移至 `lightgbm-org/LightGBM`。
+> 本仓库仍是 LightGBM 的官方源代码，由同一批维护者（包括 LightGBM 的创建者）管理。
+> 详情请参阅 https://github.com/lightgbm-org/LightGBM/issues/7187
 
 Light Gradient Boosting Machine
 ===============================
@@ -27,50 +33,50 @@ Light Gradient Boosting Machine
 [![NuGet Version](https://img.shields.io/nuget/v/lightgbm?logo=nuget&logoColor=white)](https://www.nuget.org/packages/LightGBM)
 [![Winget Version](https://img.shields.io/winget/v/Microsoft.LightGBM)](https://github.com/microsoft/winget-pkgs/tree/master/manifests/m/Microsoft/LightGBM)
 
-LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
+LightGBM 是一个采用基于树的学习算法的梯度提升（gradient boosting）框架。它面向分布式与高效场景设计，具有以下优势：
 
-- Faster training speed and higher efficiency.
-- Lower memory usage.
-- Better accuracy.
-- Support of parallel, distributed, and GPU learning.
-- Capable of handling large-scale data.
+- 更快的训练速度与更高的效率。
+- 更低的内存占用。
+- 更好的准确率。
+- 支持并行、分布式与 GPU 学习。
+- 能够处理大规模数据。
 
-For further details, please refer to [Features](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst).
+更多细节请参阅 [Features](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst).
 
-Benefiting from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/lightgbm-org/LightGBM/blob/main/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions.
+得益于这些优势，LightGBM 已广泛应用于众多机器学习竞赛的[获奖方案](https://github.com/lightgbm-org/LightGBM/blob/main/examples/README.md#machine-learning-challenge-winning-solutions)。
 
-[Comparison experiments](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, [distributed learning experiments](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
+在公开数据集上的[对比实验](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#comparison-experiment)表明，LightGBM 在效率与准确率方面均可优于现有 boosting 框架，且内存消耗显著更低。此外，[分布式学习实验](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#parallel-experiment)表明，在特定设置下，LightGBM 可通过使用多台机器进行训练实现线性加速。
 
 Get Started and Documentation
 -----------------------------
 
-Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository. If you are new to LightGBM, follow [the installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) on that site.
+我们的主要文档位于 https://lightgbm.readthedocs.io/，由本仓库生成。如果你是 LightGBM 新手，请按照该站点上的[安装说明](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html)操作。
 
-Next you may want to read:
+接下来你可能想阅读：
 
-- [**Examples**](https://github.com/lightgbm-org/LightGBM/tree/main/examples) showing command line usage of common tasks.
-- [**Features**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst) and algorithms supported by LightGBM.
-- [**Parameters**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parameters.rst) is an exhaustive list of customization you can make.
-- [**Distributed Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/GPU-Tutorial.rst) can speed up computation.
-- [**FLAML**](https://www.microsoft.com/en-us/research/project/fast-and-lightweight-automl-for-large-scale-data/articles/flaml-a-fast-and-lightweight-automl-library/) provides automated tuning for LightGBM ([code examples](https://microsoft.github.io/FLAML/docs/Examples/AutoML-for-LightGBM/)).
-- [**Optuna Hyperparameter Tuner**](https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258) provides automated tuning for LightGBM hyperparameters ([code examples](https://github.com/optuna/optuna-examples/blob/main/lightgbm/lightgbm_tuner_simple.py)).
+- [**Examples**](https://github.com/lightgbm-org/LightGBM/tree/main/examples)展示常见任务的命令行用法。
+- [**Features**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst)以及 LightGBM 支持的算法。
+- [**Parameters**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parameters.rst)是可进行自定义的详尽参数列表。
+- [**Distributed Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parallel-Learning-Guide.rst)与 [**GPU Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/GPU-Tutorial.rst)可加速计算。
+- [**FLAML**](https://www.microsoft.com/en-us/research/project/fast-and-lightweight-automl-for-large-scale-data/articles/flaml-a-fast-and-lightweight-automl-library/)为 LightGBM 提供自动化调参（[代码示例](https://microsoft.github.io/FLAML/docs/Examples/AutoML-for-LightGBM/)).
+- [**Optuna Hyperparameter Tuner**](https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258)为 LightGBM 超参数提供自动化调参（[代码示例](https://github.com/optuna/optuna-examples/blob/main/lightgbm/lightgbm_tuner_simple.py)).
 - [**Understanding LightGBM Parameters (and How to Tune Them using Neptune)**](https://neptune.ai/blog/lightgbm-parameters-guide).
 
-Documentation for contributors:
+面向贡献者的文档：
 
 - [**How we update readthedocs.io**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/README.rst).
-- Check out the [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Development-Guide.rst).
+- 查看 [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Development-Guide.rst).
 
 News
 ----
 
-Please refer to changelogs at [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases) page.
+请参阅 [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases)页面的变更日志。
 
 External (Unofficial) Repositories
 ----------------------------------
 
-Projects listed here offer alternative ways to use LightGBM.
-They are not maintained or officially endorsed by the `LightGBM` development team.
+此处列出的项目提供了使用 LightGBM 的替代方式。
+它们并非由 `LightGBM` 开发团队维护或官方背书。
 
 JPMML (Java PMML converter): https://github.com/jpmml/jpmml-lightgbm
 
@@ -92,96 +98,96 @@ daal4py (Intel CPU-accelerated inference): https://github.com/intel/scikit-learn
 
 m2cgen (model appliers for various languages): https://github.com/BayesWitnesses/m2cgen
 
-leaves (Go model applier): https://github.com/dmitryikh/leaves
+leaves（Go 模型应用器）：https://github.com/dmitryikh/leaves
 
-ONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools
+ONNXMLTools（ONNX 转换器）：https://github.com/onnx/onnxmltools
 
-SHAP (model output explainer): https://github.com/slundberg/shap
+SHAP（模型输出解释器）：https://github.com/slundberg/shap
 
-Shapash (model visualization and interpretation): https://github.com/MAIF/shapash
+Shapash（模型可视化与解释）：https://github.com/MAIF/shapash
 
-dtreeviz (decision tree visualization and model interpretation): https://github.com/parrt/dtreeviz
+dtreeviz（决策树可视化与模型解释）：https://github.com/parrt/dtreeviz
 
-supertree (interactive visualization of decision trees): https://github.com/mljar/supertree
+supertree（决策树交互式可视化）：https://github.com/mljar/supertree
 
-SynapseML (LightGBM on Spark): https://github.com/microsoft/SynapseML
+SynapseML（Spark 上的 LightGBM）：https://github.com/microsoft/SynapseML
 
-Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing
+Kubeflow Fairing（Kubernetes 上的 LightGBM）：https://github.com/kubeflow/fairing
 
-Kubeflow Operator (LightGBM on Kubernetes): https://github.com/kubeflow/xgboost-operator
+Kubeflow Operator（Kubernetes 上的 LightGBM）：https://github.com/kubeflow/xgboost-operator
 
-lightgbm_ray (LightGBM on Ray): https://github.com/ray-project/lightgbm_ray
+lightgbm_ray（Ray 上的 LightGBM）：https://github.com/ray-project/lightgbm_ray
 
-Ray (distributed computing framework): https://github.com/ray-project/ray
+Ray（分布式计算框架）：https://github.com/ray-project/ray
 
-Mars (LightGBM on Mars): https://github.com/mars-project/mars
+Mars（Mars 上的 LightGBM）：https://github.com/mars-project/mars
 
-ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning
+ML.NET（.NET/C# 包）：https://github.com/dotnet/machinelearning
 
-LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net
+LightGBM.NET（.NET/C# 包）：https://github.com/rca22/LightGBM.Net
 
-LightGBM Ruby (Ruby gem): https://github.com/ankane/lightgbm-ruby
+LightGBM Ruby（Ruby gem）：https://github.com/ankane/lightgbm-ruby
 
-LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j
+LightGBM4j（Java 高级绑定）：https://github.com/metarank/lightgbm4j
 
-LightGBM4J (JVM interface for LightGBM written in Scala): https://github.com/seek-oss/lightgbm4j
+LightGBM4J（用 Scala 编写的 LightGBM JVM 接口）：https://github.com/seek-oss/lightgbm4j
 
-Julia-package: https://github.com/IQVIA-ML/LightGBM.jl
+Julia-package：https://github.com/IQVIA-ML/LightGBM.jl
 
-lightgbm3 (Rust binding): https://github.com/Mottl/lightgbm3-rs
+lightgbm3（Rust 绑定）：https://github.com/Mottl/lightgbm3-rs
 
-MLServer (inference server for LightGBM): https://github.com/SeldonIO/MLServer
+MLServer（LightGBM 推理服务器）：https://github.com/SeldonIO/MLServer
 
-MLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow
+MLflow（实验跟踪、模型监控框架）：https://github.com/mlflow/mlflow
 
-FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML
+FLAML（用于超参数优化的 AutoML 库）：https://github.com/microsoft/FLAML
 
-MLJAR AutoML (AutoML on tabular data): https://github.com/mljar/mljar-supervised
+MLJAR AutoML（表格数据上的 AutoML）：https://github.com/mljar/mljar-supervised
 
-Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna
+Optuna（超参数优化框架）：https://github.com/optuna/optuna
 
-LightGBMLSS (probabilistic modelling with LightGBM): https://github.com/StatMixedML/LightGBMLSS
+LightGBMLSS（基于 LightGBM 的概率建模）：https://github.com/StatMixedML/LightGBMLSS
 
-LightGBM-MoE (Mixture-of-Experts / regime-switching extension): https://github.com/kyo219/LightGBM-MoE
+LightGBM-MoE（专家混合 / 状态切换扩展）：https://github.com/kyo219/LightGBM-MoE
 
-darts (time series forecasting and anomaly detection with LightGBM): https://github.com/unit8co/darts
+darts（使用 LightGBM 进行时间序列预测与异常检测）：https://github.com/unit8co/darts
 
-mlforecast (time series forecasting with LightGBM): https://github.com/Nixtla/mlforecast
+mlforecast（使用 LightGBM 进行时间序列预测）：https://github.com/Nixtla/mlforecast
 
-skforecast (time series forecasting with LightGBM): https://github.com/JoaquinAmatRodrigo/skforecast
+skforecast（使用 LightGBM 进行时间序列预测）：https://github.com/JoaquinAmatRodrigo/skforecast
 
-`{bonsai}` (R `{parsnip}`-compliant interface): https://github.com/tidymodels/bonsai
+`{bonsai}`（符合 R `{parsnip}` 规范的接口）：https://github.com/tidymodels/bonsai
 
-`{mlr3extralearners}` (R `{mlr3}`-compliant interface): https://github.com/mlr-org/mlr3extralearners
+`{mlr3extralearners}`（符合 R `{mlr3}` 规范的接口）：https://github.com/mlr-org/mlr3extralearners
 
-lightgbm-transform (feature transformation binding): https://github.com/lightgbm-org/LightGBM-transform
+lightgbm-transform（特征转换绑定）：https://github.com/lightgbm-org/LightGBM-transform
 
-`postgresml` (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml
+`postgresml`（通过 Postgres 扩展在 SQL 中进行 LightGBM 训练与预测）：https://github.com/postgresml/postgresml
 
-`pyodide` (run `lightgbm` Python-package in a web browser): https://github.com/pyodide/pyodide
+`pyodide`（在 Web 浏览器中运行 `lightgbm` Python 包）：https://github.com/pyodide/pyodide
 
-`vaex-ml` (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex
+`vaex-ml`（自带 LightGBM 接口的 Python DataFrame 库）：https://github.com/vaexio/vaex
 
-Support
+支持
 -------
 
-- Ask a question [on Stack Overflow with the `lightgbm` tag](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions.
-- Open **bug reports** and **feature requests** on [GitHub issues](https://github.com/lightgbm-org/LightGBM/issues).
+- 在 [Stack Overflow 上使用 `lightgbm` 标签提问](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions.
+- 在 [GitHub issues](https://github.com/lightgbm-org/LightGBM/issues). 上提交 **bug 报告** 和 **功能请求**。
 
-How to Contribute
+如何贡献
 -----------------
 
-Check [CONTRIBUTING](https://github.com/lightgbm-org/LightGBM/blob/main/CONTRIBUTING.md) page.
+请参阅 [CONTRIBUTING](https://github.com/lightgbm-org/LightGBM/blob/main/CONTRIBUTING.md) 页面。
 
-Microsoft Open Source Code of Conduct
+Microsoft 开源行为准则
 -------------------------------------
 
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+本项目已采纳 [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 更多信息请参阅 [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)，或通过 [opencode@microsoft.com](mailto:opencode@microsoft.com) 联系我们提出其他问题或意见。
 
-Reference Papers
+参考论文
 ----------------
 
-Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu. "Quantized Training of Gradient Boosting Decision Trees" ([link](https://proceedings.neurips.cc/paper/2022/hash/77911ed9e6e864ca1a3d165b2c3cb258-Abstract.html)). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 18822-18833.
+Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu. "Quantized Training of Gradient Boosting Decision Trees"（[链接](https://proceedings.neurips.cc/paper/2022/hash/77911ed9e6e864ca1a3d165b2c3cb258-Abstract.html)). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 18822-18833.
 
 Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. "[LightGBM: A Highly Efficient Gradient Boosting Decision Tree](https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html)". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157.
 
@@ -189,7 +195,7 @@ Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu.
 
 Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boosting](https://arxiv.org/abs/1706.08359)". SysML Conference, 2018.
 
-License
+许可证
 -------
 
-This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/lightgbm-org/LightGBM/blob/main/LICENSE) for additional details.
+本项目依据 MIT 许可证条款授权。更多详情请参阅 [LICENSE](https://github.com/lightgbm-org/LightGBM/blob/main/LICENSE)。